About this Research Topic
The human brain, consisting of about 100 billion neurons connected by an estimated 100 trillion synapses, supports us to make sense of a complex and ever-changing sensory world and to make complex decisions and actions. It is the most intelligent biological computing ‘machine’ at a fundamental level. Against this backdrop, neuroscience-inspired intelligent computing, like artificial neural network (ANN) and brain computer interface (BCI), has made dramatic advances in the last decades. It has been increasingly applied to a range of practical problems, such as natural language processing, visual analysis, speech recognition, robot control, language translation, etc. It plays a crucial role in promoting the rapid development of science and technology.
The human brain is an extremely complex and intelligent computational system. Although the opportunities of cross-pollination between human brain and intelligent computing are great, the road to achieving brain-like intelligent computing is still long and rocky. The objective of this Research Topic is to explore the latest up-to-date theory, methods of neuroscience-inspired intelligent computing, and their applications in improving the intelligence level of machines. Both theoretical and applied results with algorithms and applications are desired. This Research Topic offers a concentrative venue for researchers to make rapid exchange of ideas and original research findings in neuroscience-inspired intelligent computing.
We encourage researchers and experts worldwide to contribute by submitting high-quality original research papers and systematic review papers. New interdisciplinary approaches, open-source tools, open-source datasets regarding neuroscience-inspired intelligent computing are especially welcome. Areas to be covered in this Research Topic may include, but are not limited to:
1. Brain-like computing
2. Cognitive computing
3. Fuzzy computing
4. Representation learning with neuroscience
5. Data mining with neuroscience
6. Applications of neuroscience in intelligent computing
7. New techniques, algorithms, applications with ANN or BCI
8. Human-Robot Interaction
9. Intelligent robots and systems
10. Human-Machine systems
11. Information security with neuroscience
The human brain is an extremely complex and intelligent computational system. Although the opportunities of cross-pollination between human brain and intelligent computing are great, the road to achieving brain-like intelligent computing is still long and rocky. The objective of this Research Topic is to explore the latest up-to-date theory, methods of neuroscience-inspired intelligent computing, and their applications in improving the intelligence level of machines. Both theoretical and applied results with algorithms and applications are desired. This Research Topic offers a concentrative venue for researchers to make rapid exchange of ideas and original research findings in neuroscience-inspired intelligent computing.
We encourage researchers and experts worldwide to contribute by submitting high-quality original research papers and systematic review papers. New interdisciplinary approaches, open-source tools, open-source datasets regarding neuroscience-inspired intelligent computing are especially welcome. Areas to be covered in this Research Topic may include, but are not limited to:
1. Brain-like computing
2. Cognitive computing
3. Fuzzy computing
4. Representation learning with neuroscience
5. Data mining with neuroscience
6. Applications of neuroscience in intelligent computing
7. New techniques, algorithms, applications with ANN or BCI
8. Human-Robot Interaction
9. Intelligent robots and systems
10. Human-Machine systems
11. Information security with neuroscience
Keywords: Neuroscience, Intelligent computing, Brain-like computing, Cognitive computing, Fuzzy computing
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
